Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)
Kazuyuki Samejima, Kenji Doya, Yasumasa Ueda, Minoru Kimura
When we model a higher order functions, such as learning and memory, we face a difﬁculty of comparing neural activities with hidden variables that depend on the history of sensory and motor signals and the dynam- ics of the network. Here, we propose novel method for estimating hidden variables of a learning agent, such as connection weights from sequences of observable variables. Bayesian estimation is a method to estimate the posterior probability of hidden variables from observable data sequence using a dynamic model of hidden and observable variables. In this pa- per, we apply particle ﬁlter for estimating internal parameters and meta- parameters of a reinforcement learning model. We veriﬁed the effective- ness of the method using both artiﬁcial data and real animal behavioral data.